You're reading from Data Engineering with AWS - Second Edition

Product type Book

Published in Oct 2023

Publisher Packt

ISBN-13 9781804614426

Pages 636 pages

Edition 2nd Edition

Languages

Concepts

Data Engineering

Author (1):

Gareth Eagar

Table of Contents (24) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. An Introduction to Data Engineering

3. Data Management Architectures for Analytics

4. The AWS Data Engineer’s Toolkit

5. Data Governance, Security, and Cataloging

6. Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations

7. Architecting Data Engineering Pipelines

8. Ingesting Batch and Streaming Data

9. Transforming Data to Optimize for Analytics

10. Identifying and Enabling Data Consumers

11. A Deeper Dive into Data Marts and Amazon Redshift

12. Orchestrating the Data Pipeline

13. Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

14. Ad Hoc Queries with Amazon Athena

15. Visualizing Data with Amazon QuickSight

16. Enabling Artificial Intelligence and Machine Learning

17. Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World

18. Building Transactional Data Lakes

19. Implementing a Data Mesh Strategy

20. Building a Modern Data Platform on AWS

21. Wrapping Up the First Part of Your Learning Journey

22. Other Books You May Enjoy

23. Index

AWS services that help enable a data mesh approach

Most analytics vendors have been adding functionality to their solutions to support a data mesh approach over the past few years. And while there is currently no single solution that enables a data mesh across a complex selection of analytical tools and hybrid environments, many companies have made good progress in supporting the data mesh approach, at least within their own “ecosystem” of tools.

AWS has supported sharing both S3-and Redshift-based datasets across AWS accounts for a while. And while easy sharing of data across different AWS accounts (and different teams within an organization generally have their own AWS accounts) is a key component of a data mesh architecture, it is only a piece of building a data mesh. Another key component is the ability to centrally catalog data and add rich business metadata for each dataset, which can be done with the Amazon DataZone service. In this section, we explore the...